Year: 2025
Type: article
Abstract: - This paper introduces BiasNet, a comprehensive, end-to-end data analysis tool designed to uncover hidden biases and inequities in datasets without requiring pre-existing labels. BiasNet serves as an exploratory engine, leveraging a suite of unsupervised machine learning algorithms to segment a population into naturally occurring groups. By analyzing the demographic composition and characteristics of these discovered clusters, the tool quantifies potential disparities across sensitive attributes. The methodology encompasses versatile data ingestion, advanced preprocessing for structured and text data, a comprehensive suite of clustering algorithms, and the calculation of unsupervised fairness metrics. The entire workflow is encapsulated in an interactive web interface, culminating in a detailed, AI-generated PDF report with rich visualizations, making data auditing accessible to data scientists, analysts, and decision-makers. Key Words: unsupervised learning, bias detection, algorithmic fairness, data auditing, clustering, explainable AI, responsible AI
DOI: 10.55041/ijsrem52445
Authors: 1Independent Researcher, Hyderabad, India, Sudhanshu Ranshevare, Anmol Devansh
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
SDG: Reduced inequalities
Open Access status: bronze
OpenAlex ID: W4414000044